We use the following approaches for comparison:
BC+FT: Trains a behavioral cloning agent (i.e. \(\pi _\theta (a \vert s)\) ) on the offline dataset \(\mathcal {D}\)  and fine-tunes to the downstream dataset \(\mathcal {D}^{\text{demo}}\) .
SPiRL: This is an extension of the existing skill extraction methods to imitation learning over skill space [1]}, [2]}. SPiRL [2]} is very similar to our skill extraction method, but instead of conditioning the skill prior on the future state it only uses the current state. We extract skills from \(\mathcal {D}\)  using SPiRL, fine-tune the module on the downstream demonstrations \(\mathcal {D}^{\text{demo}}\) , and then execute the skill prior for evaluation. FIST (ours): This runs our semi-parametric approach after learning the future conditioned skill prior. After extracting skills from \(\mathcal {D}\)  we fine-tune the parameters on the downstream demonstrations \(\mathcal {D}^{\text{demo}}\)  and perform the proposed semi-parametric approach for evaluation.
